Estimating the pose of an uncooperative spacecraft is an important computer vision problem for enabling the deployment of automatic vision-based systems in orbit, with applications ranging from on-orbit servicing to space debris removal. Following the general trend in computer vision, more and more works have been focusing on leveraging Deep Learning (DL) methods to address this problem. However and despite promising research-stage results, major challenges preventing the use of such methods in real-life missions still stand in the way. In particular, the deployment of such computation-intensive algorithms is still under-investigated, while the performance drop when training on synthetic and testing on real images remains to mitigate. The primary goal of this survey is to describe the current DL-based methods for spacecraft pose estimation in a comprehensive manner. The secondary goal is to help define the limitations towards the effective deployment of DL-based spacecraft pose estimation solutions for reliable autonomous vision-based applications. To this end, the survey first summarises the existing algorithms according to two approaches: hybrid modular pipelines and direct end-to-end regression methods. A comparison of algorithms is presented not only in terms of pose accuracy but also with a focus on network architectures and models' sizes keeping potential deployment in mind. Then, current monocular spacecraft pose estimation datasets used to train and test these methods are discussed. The data generation methods: simulators and testbeds, the domain gap and the performance drop between synthetically generated and lab/space collected images and the potential solutions are also discussed. Finally, the paper presents open research questions and future directions in the field, drawing parallels with other computer vision applications.

翻译：估计非合作航天器的姿态是一个重要的计算机视觉问题，旨在实现在轨自主视觉系统的部署，其应用涵盖在轨服务到空间碎片清除。紧随计算机视觉的总体趋势，越来越多的研究致力于利用深度学习（DL）方法解决这一问题。然而，尽管研究阶段成果令人鼓舞，阻碍此类方法在真实任务中应用的主要挑战仍然存在。特别是，此类计算密集型算法的部署仍待深入研究，而合成图像训练与真实图像测试之间的性能下降问题尚未解决。本综述的首要目标是全面描述当前基于深度学习的航天器姿态估计方法。第二个目标是明确有效部署基于深度学习的航天器姿态估计方案以构建可靠自主视觉应用所面临的局限。为此，本文首先根据两种方法对现有算法进行总结：混合模块化流水线与直接端到端回归方法。除姿态精度外，本文还从网络架构与模型尺寸的角度进行算法对比，重点考虑未来部署需求。随后，讨论了当前用于训练和测试这些方法的单目航天器姿态估计数据集。进一步探讨了数据生成方法（模拟器与测试台）、域差异及合成图像与实验室/空间采集图像之间的性能下降问题，并分析了潜在解决方案。最后，本文通过与其他计算机视觉应用的类比，提出了该领域尚待解决的研究问题与未来发展方向。